Clinical Evidence and RWE Pipelines
Consequential Data Engineering
Data cataloging, governance, data cleansing with an audit trail aligned to 21 CFR Part 11, and clinical evidence datasets and documentation that support a sponsor's 510(k), De Novo or PMA submission, for organizations where data provenance under HIPAA and ALCOA+ matters as much as model performance.
What do clinical evidence and AI data services include?
They include data cataloging, governance, data cleansing with an audit trail aligned to 21 CFR Part 11, and evidence datasets that support a sponsor's 510(k), De Novo or PMA submission. They serve organizations where data provenance under HIPAA and ALCOA+ matters as much as model performance.
Data cataloging, governance, data cleansing with Part 11-aligned audit trails, and clinical evidence preparation for organizations navigating FDA premarket submissions, HIPAA boundaries, and algorithmic validation — where data provenance matters as much as model performance.
The Customer Problem: Four Clinical Evidence Failure Patterns
Lineage lost between extract and analysis table
A study dataset is assembled by scripts on an analyst's machine. When a reviewer asks where a value came from, nobody can walk it back to the EHR export it started in.
A de-identified dataset that quietly stops being one
A Safe Harbor extract is joined back to a table that still holds full dates of service or five-digit ZIP codes, and the result no longer meets the standard it was released under.
Mappings checked on a sample, wrong on real data
An OMOP or FHIR mapping passes against a curated sample. Local codes the sample never contained land in an unmapped bucket that nobody counts.
Cleansing nobody can reproduce
Outlier and imputation rules are applied by hand, so running the pipeline again produces a different analysis table and the result cannot be defended.
Four commitments that hold across every engagement
- Provenance-First
- Every dataset's lineage and PHI/ePHI boundary mapped before a single transform runs.
- Audit-Ready Cleansing
- Automated and expert-led cleansing validated against ALCOA+ data-integrity principles.
- Protocol Interoperability
- HL7 FHIR, DICOM, and OMOP mappings that hold up against a clinical system's actual data.
- Submission-Grade Traceability
- Evidence dossiers a reviewer can trace from raw source to submitted dataset.
Built for
- Academic Medical Centers
- Clinical Research Teams
- Medical Device Companies
- Pharmaceutical & Biotech
Technical Architecture: The Clinical Evidence Pipeline
What Netspective Offers: Engineering Responsibility
Six capability areas
Data Discovery & Cataloging
Metadata tagging and data lineage tracking so every dataset's origin and transformation history stays visible to a reviewer.
- Metadata Tagging
- Data Lineage Tracking
- PHI/ePHI Boundary Mapping
Regulatory Governance & Compliance
21 CFR Part 11, HIPAA Safe Harbor / Expert Determination, and GDPR health-data controls are applied to the pipeline itself, with an audit trail for PHI, not bolted on after.
- 21 CFR Part 11 Controls
- HIPAA Safe Harbor / Expert Determination
- GDPR Health Data Controls
Automated & Expert-Led Cleansing
Anomaly detection and missing-value imputation protocols reviewed against ALCOA+ data-integrity principles, not applied as a black box.
- Anomaly Detection
- Missing-Value Imputation Protocols
- ALCOA+ Data-Integrity Validation
Clinical System Interoperability
HL7 FHIR bulk data export, DICOM image metadata extraction, and OMOP common data model mapping against a client's own source systems.
- HL7 FHIR Bulk Data Export
- DICOM Image Metadata Extraction
- OMOP Common Data Model Mapping
Biostatistical & Model Validation
Dataset splitting protocols, algorithmic bias testing, and performance metric reproducibility, documented for a statistical reviewer.
- Dataset Splitting Protocols
- Algorithmic Bias Testing
- Performance Metric Reproducibility
Submission Evidence Dossiers
Clinical evaluation report datasets, statistical analysis plans (SAP), and audit-traced evidence packages assembled for the submission itself.
- Clinical Evaluation Report Datasets
- Statistical Analysis Plans (SAP)
- Audit-Traced Evidence Packages
Ready to Build?
Discuss a clinical data pipeline or submission dataset for a specific regulated program.
Engineering reference only. Not formal regulatory counsel. Specific de-identification method (Safe Harbor vs. Expert Determination) and data model mappings are scoped per engagement against the client's own data governance program.
Mechanics and Data Contracts
Each boundary in the pipeline has a written contract and a check that shows it holds. The checks run in your environment; the queries are in the explorer below.
| Interface | Contract | Checked by |
|---|---|---|
| Source extract to raw landing | Each extract kept unchanged, with its SHA-256 checksum and the date it was received | The checksum manifest (Panel 1) |
| Raw data to the standard model (OMOP CDM or FHIR R4) | Every source code is mapped to a standard concept or listed as unmapped with its record count | The unmapped-code query (Panel 2) |
| PHI boundary | Identifiers listed per column, and the de-identification method recorded per dataset: Safe Harbor (45 CFR §164.514(b)(2)) or Expert Determination (§164.514(b)(1)) | A column inventory reviewed before release |
| Analysis table to source | Every row carries the keys of its source records and the extract they came from | The lineage query (Panel 3) |
| Cleansing and imputation rules | Rules are versioned; a rule change re-runs the pipeline and the two analysis tables are compared | A recorded re-run comparison |
Audit trails for electronic records follow 21 CFR Part 11 §11.10(e) where the sponsor's validated process requires them. Data integrity is judged against ALCOA+: attributable, legible, contemporaneous, original and accurate, plus complete, consistent, enduring and available.
Provenance and Mapping Explorer
Three steps a reviewer can re-run. Each panel says whether it is a public command or a synthetic illustration, and every query is SQLite.
Panel 1: Record a checksum for every raw extract
Before any transform runs, record what arrived. The manifest is what later shows a raw copy is unchanged.
# Record a SHA-256 checksum for every raw extract before any transform runs
sha256sum extracts/*.csv extracts/*.ndjson > extracts.sha256
# Later: show each raw copy is unchanged (prints OK or FAILED per file)
sha256sum --check extracts.sha256
Public command Writes: extracts.sha256, one line per file. sha256sum is part of GNU coreutils; on macOS use shasum -a 256. A checksum shows a file is unchanged; it says nothing about whether the data in it is right.
Panel 2: Source codes with no standard mapping
Every local or source code that no concept map covers, with the number of records it affects, highest first.
-- Synthetic illustration: source codes with no mapping to a standard
-- concept, most frequent first. These are the records a sample-based
-- mapping check never sees.
CREATE TEMP TABLE IF NOT EXISTS source_observation (
record_id TEXT PRIMARY KEY,
source_system TEXT NOT NULL,
source_vocabulary TEXT NOT NULL,
source_code TEXT NOT NULL
);
CREATE TEMP TABLE IF NOT EXISTS concept_map (
source_vocabulary TEXT NOT NULL,
source_code TEXT NOT NULL,
target_concept_id INTEGER NOT NULL,
PRIMARY KEY (source_vocabulary, source_code)
);
SELECT o.source_system,
o.source_vocabulary,
o.source_code,
COUNT(*) AS records
FROM source_observation o
LEFT JOIN concept_map m
ON m.source_vocabulary = o.source_vocabulary
AND m.source_code = o.source_code
WHERE m.target_concept_id IS NULL
GROUP BY o.source_system, o.source_vocabulary, o.source_code
ORDER BY records DESC, o.source_code;
Synthetic illustration TEMP stand-ins with an illustrative layout, not a product schema; nothing is written. Run it on the full source data, not the sample the mapping was built from. Runs as written in SQLite 3.
Panel 3: Dataset rows with no recorded source
Rows in one study dataset that cannot be traced to a source record in an extract the manifest knows about.
-- Synthetic illustration: rows in a study dataset that cannot be traced to
-- a raw extract recorded in the checksum manifest.
CREATE TEMP TABLE IF NOT EXISTS raw_extract (
extract_sha256 TEXT PRIMARY KEY,
file_name TEXT NOT NULL,
received_utc TEXT NOT NULL
);
CREATE TEMP TABLE IF NOT EXISTS analysis_row (
row_id INTEGER PRIMARY KEY,
dataset_id TEXT NOT NULL
);
CREATE TEMP TABLE IF NOT EXISTS row_source (
row_id INTEGER NOT NULL REFERENCES analysis_row(row_id),
source_record_id TEXT NOT NULL,
extract_sha256 TEXT NOT NULL,
PRIMARY KEY (row_id, source_record_id)
);
SELECT a.dataset_id,
a.row_id
FROM analysis_row a
WHERE a.dataset_id = 'study-001'
AND NOT EXISTS (
SELECT 1
FROM row_source s
JOIN raw_extract e ON e.extract_sha256 = s.extract_sha256
WHERE s.row_id = a.row_id
)
ORDER BY a.row_id;
Synthetic illustration TEMP stand-ins; nothing is written. An empty result means every row traces to a recorded extract; a reviewer still samples rows against the source. Runs as written in SQLite 3.
Verified Proof Ledger (Engineering Ground Truth)
| Claim | Status | What it rests on |
|---|---|---|
| HL7 v2, FHIR R4 and C-CDA validation gate with a triage queue | Built and tested | Built and tested in the Healthcare Data Lake pipeline (specs/049 proof ledger). |
| Raw copies kept in your own cloud account under your KMS or Key Vault keys | Built and tested | Built and tested in the Healthcare Data Lake pipeline (specs/049 proof ledger). |
| FHIR, DICOM and OMOP mappings with recorded lineage for a study dataset | Designed and documented | Engagement method; no public client dataset to show. |
| Evidence packages that trace each dataset to its sources (ALCOA+) | Designed and documented | Engagement method; delivered per study or submission. |
| A clearance, approval or study conclusion | Not established | FDA and the sponsor decide; we supply datasets and documentation. |
| 21 CFR Part 11 compliance of the pipeline itself | Not established | Part 11 controls belong to the validated process the pipeline runs in. |
Typical Engagements
These describe the shape of an engagement in each setting. They are not named client results.
Device sponsor: real-world evidence for a 510(k)
Registry and EHR data for a predicate comparison, mapped to OMOP with every unmapped code counted and every analysis row traced to its extract before the statistician runs the analysis plan.
Academic medical center: OMOP conversion for a research network
Local codes from several source systems mapped to standard concepts, with the unmapped list reviewed at each release instead of discovered by a network query.
Validation dataset for an AI-enabled device
Training, tuning and test sets split by patient rather than by record, so the same person never appears on both sides, with the split recorded and reproducible.
Scoped Pilot: The 2-week provenance and dataset readiness review
What the two weeks deliver
Week 1
Map the PHI boundary and the sources behind one study or submission question: EHR exports, FHIR APIs, DICOM imaging or an OMOP database.
Week 2
Trace one dataset from source to analysis table, then deliver a gap list against ALCOA+ expectations.
Boundaries
- Engineering and documentation support, not regulatory or legal counsel. The sponsor owns the submission and its conclusions.
- PHI stays in your environment. Data access is agreed in writing before any work starts.
- No outcome is promised: FDA, IRBs and reviewers decide.